Impact of dysfunctional ubiquitination in response to cancer immunotherapy
Somatic mutations analysis - Functional Impact Prediction
Somatic mutations analysis - Functional Impact Prediction
Eric Bautista Farrerons (s212514)
First created on January 2024. Updated on 18 June 2024
1 Load data
### Load packages, functions and paths
source(file = "../01_install.R")
source(file = "../02_functions.R")
source(file = "../03_paths.R")
# MAF-like file
maf_data <- "41467_2017_1460_MOESM6_ESM_somatic_mutations.xlsx"
maf_path <- file.path(data_path,
maf_data)
# Sup1 data
sup1_response_path <- file.path(results_pc_path,
"sup1_response.csv")
### Define variables
nonsyn_categories <- c("Missense_Mutation", "Nonsense_Mutation", "Nonstop_Mutation", "Start_Codon_SNP", "Splice_Site")
### Read data & wrangle
## Read maf-like file
maf_df <- read_excel(maf_path,
skip=1,
col_names=TRUE)
# Add a sample ID column
maf_df <- maf_df %>%
dplyr::mutate(sample_id = sub("_[12]$", "", tumor_name)) %>%
dplyr::rename(entrez_id = Entrez_Gene_Id,
hugo_symbol = Hugo_Symbol) %>%
dplyr::mutate(hugo_symbol = replace(hugo_symbol, hugo_symbol == "Unknown", NA),
entrez_id = replace(entrez_id, entrez_id == 0, NA)) %>%
dplyr::select(sample_id, entrez_id, hugo_symbol, everything()) %>%
dplyr::arrange(sample_id) %>%
dplyr::distinct() %>% # Drops 2 rows that were duplicated in the original dataset
dplyr::filter(!is.na(hugo_symbol) | !is.na(entrez_id)) # Drop where both Entrez, Hugo have NAs
# Convert Hugo Symbols to official naming
## Step 1: Filter out rows with NA in hugo_symbol
hugo_symbols <- maf_df %>%
dplyr::filter(!is.na(hugo_symbol)) %>%
dplyr::pull(hugo_symbol) %>%
as.character()
## Step 2: Convert to official name
ofc_hugo_symbols <- alias2SymbolTable(hugo_symbols, species = "Hs")
## Step 3: Add the ofc_hugo_symbols to the dataframe
maf_df$update_indicator <- !is.na(maf_df$hugo_symbol) & maf_df$hugo_symbol %in% hugo_symbols # Create an temp indicator for rows to update
maf_df <- maf_df %>%
dplyr::mutate(hugo_symbol = ifelse(update_indicator, ofc_hugo_symbols[match(hugo_symbol, hugo_symbols)], hugo_symbol)) %>%
dplyr::select(-update_indicator) %>% # Remove the indicator column after updating
dplyr::filter(!is.na(hugo_symbol) | !is.na(entrez_id)) # Drop where both Entrez, Hugo have NAs (again)
## Read sup1_response.csv
sup1_response_df <- read.csv(sup1_response_path)[ , -1] # Drop first column
# Create a list of Responders and Non-responders
sample_id_lists <- split(sup1_response_df$Sample.ID,
sup1_response_df$Patient.Response) # Split the "Sample.ID" values into lists based on "Patient.Response"
r_sample_ids <- sample_id_lists$R
nr_sample_ids <- sample_id_lists$NR2 Initial considerations
# Filter rows where entrez_id is 0
missing_entrez_df <- maf_df %>%
dplyr::filter(is.na(entrez_id))
# Filter rows where Hugo_Symbol is Unknown
missing_hugo_df <- maf_df %>%
dplyr::filter(is.na(hugo_symbol))
n_rows_1 <- nrow(missing_entrez_df)
n_rows_2 <- nrow(missing_hugo_df)Number of genomic regions missing an Entrez ID: 94
Number of genomic regions missing Hugo Symbol: 924
# How many different types of mutations has the dataset?
unique_values <- unique(maf_df$Variant_Classification)
# For selected nonsynonymous mutations based on literature
selected_nonsyn_mutations <- c(unique_values[1], unique_values[2], unique_values[7], unique_values[10], unique_values[12])The mutation categories in the data are: Missense_Mutation, Silent, Splice_Site, Intron, 5’UTR, RNA, Nonsense_Mutation, IGR, 3’UTR, Start_Codon_SNP, lincRNA, Nonstop_Mutation
The mutation categories I will use as Nonsynonymous based on literature: Missense_Mutation, Silent, Nonsense_Mutation, Start_Codon_SNP, Nonstop_Mutation
3 Re-annotate Entrez IDs, Hugo Symbols, Ensembl Gene IDs
maf_df_annotated <- maf_df
#1 Re-annotate Hugo Symbols with Entrez IDs
## Step 1: Filter in rows with NA in hugo_symbol
entrez_ids <- maf_df_annotated %>%
dplyr::filter(is.na(hugo_symbol)) %>%
dplyr::pull(entrez_id) %>%
as.character()
## Step 2: Map Entrez IDs to Hugo Symbols
new_hugo_symbols <- ENTREZtoSYMBOL(entrez_ids)
## Step 3: Add the new_hugo_symbols to the dataframe
maf_df_annotated$update_indicator <- is.na(maf_df_annotated$hugo_symbol) & maf_df_annotated$entrez_id %in% entrez_ids # Create an temp indicator for rows to update
maf_df_annotated <- maf_df_annotated %>%
dplyr::mutate(hugo_symbol = ifelse(update_indicator, new_hugo_symbols[match(entrez_id, entrez_ids)], hugo_symbol)) %>%
dplyr::select(-update_indicator) # Remove the indicator column after updating
#2 Re-annotate Entrez IDs with Hugo Symbols
## Step 1: Filter in rows with NA in entrez_id
hugo_symbols <- maf_df_annotated %>%
dplyr::filter(is.na(entrez_id)) %>%
dplyr::pull(hugo_symbol) %>%
as.character()
## Step 2: Map Hugo Symbols to Entrez IDs
new_entrez_ids <- SYMBOLtoENTREZ(hugo_symbols)
## Step 3: Add the new_entrez_ids to the dataframe
maf_df_annotated$update_indicator <- is.na(maf_df_annotated$entrez_id) & maf_df_annotated$hugo_symbol %in% hugo_symbols # Create an temp indicator for rows to update
maf_df_annotated <- maf_df_annotated %>%
dplyr::mutate(entrez_id = ifelse(update_indicator, new_entrez_ids[match(hugo_symbol, hugo_symbols)], entrez_id)) %>%
dplyr::select(-update_indicator) # Remove the indicator column after updating
#3 Annotate Ensembl Gene IDs with Entrez IDs
## Step 1: Get entrez_ids
entrez_ids <- maf_df_annotated %>%
dplyr::pull(entrez_id) %>%
as.character()
## Step 2: Map Entrez IDs to Ensembl Gene IDs
ensembl_ids <- ENTREZtoENSEMBL(entrez_ids)
## Step 3: Add the new_entrez_ids to the dataframe
maf_df_annotated <- maf_df_annotated %>%
dplyr::mutate(ensembl_id = ensembl_ids) %>%
dplyr::select(sample_id, ensembl_id, entrez_id, hugo_symbol, everything()) %>%
dplyr::filter(!(sample_id %in% c("MM909_20", "MM909_24")))
# Save RDS
maf_df_anno_path <- file.path(results_fip_path, "maf_df_annotated.rds")
saveRDS(maf_df_annotated, maf_df_anno_path)There is a “problem” here: some Entrez IDs are mapped to several Ensembl Gene IDs. I need to resolve this because otherwise it will impact the TMB calculation and possibly other analyses. I decided to keep the first Ensembl Gene ID that is mapped.
4 Get a list of mutations for each patient
# Splitting the dataframe into a list based on Sample.ID
list_of_all_mutations <- split(maf_df_annotated, maf_df_annotated$sample_id)
# Save the maf_df_annotated into a file
maf_out_path <- file.path(results_fip_path, "all_mutations.csv")
write.csv(maf_df_annotated, file = maf_out_path, row.names = TRUE)5 Get a list of Nonsynonymous mutations per patient
nonsyn_maf_df <- maf_df_annotated %>%
dplyr::filter(Variant_Classification %in% nonsyn_categories) %>%
dplyr::arrange(sample_id)
list_of_nonsyn_mutations <- split(nonsyn_maf_df, nonsyn_maf_df$sample_id)
# Save the nonsyn_maf_df into a file
nonsyn_maf_out_path <- file.path(results_fip_path,
"nonsyn_mutations.csv")
write.csv(nonsyn_maf_df, file = nonsyn_maf_out_path, row.names = TRUE)6 Export info to use Ensembl VEP (web version)
### Extract nonsyn info in correct format for VEP input
api_input <- nonsyn_maf_df %>%
dplyr::select(Chromosome,
Start_position,
End_position,
ref_allele,
alt_allele,
Strand,
sample_id) %>%
dplyr::mutate(Allele_ref_alt = paste(ref_allele,
alt_allele,
sep = "/")) %>%
dplyr::mutate(API_info = paste(Chromosome,
Start_position,
End_position,
Allele_ref_alt,
Strand,
sample_id,
sep = " "))
### Export the info to a file and use the Web VEP
vep_input_data <- "../vep_input_nonsyn_grch37.txt"
vep_file_path <- file.path(data_path,
vep_input_data)
write.table(api_input$API_info, file = vep_file_path, row.names = FALSE, col.names = FALSE, quote = FALSE)7 Run VEP and explore output
### Define path
vep_out_data <- "../vep_output_nonsyn_grch37.txt"
vep_path <- file.path(data_path,
vep_out_data)
### Read data
vep_df <- read.csv(vep_path, header = TRUE, sep = "\t")
### Clean and wrangle
vep_df_clean <- vep_df %>%
dplyr::mutate(across(everything(), ~na_if(., "-"))) %>%
dplyr::select(where(~any(!is.na(.)))) %>%
dplyr::rename(sample_id = X.Uploaded_variation,
ensembl_id = Gene) %>%
dplyr::filter(!(sample_id %in% c("MM909_20", "MM909_24"))) %>%
dplyr::mutate(
SIFT_cleaned = str_extract(SIFT, "^[^(]+"),
PolyPhen_cleaned = str_extract(PolyPhen, "^[^(]+"),
Liberal_consequence = case_when(
SIFT_cleaned %in% c("deleterious", "deleterious_low_confidence") |
PolyPhen_cleaned %in% c("probably_damaging", "possibly_damaging") ~ "deleterious",
TRUE ~ "non-deleterious"
)
)The columns that do not have any information are: X.Uploaded_variation, Gene, MANE_SELECT, MANE_PLUS_CLINICAL, TSL, APPRIS, UNIPROT_ISOFORM, HGVS_OFFSET
The consideration of LoF mutations has been made in a “liberal” manner, meaning that if either SIFT or PolyPhen scores indicated the mutation to be deleterious, the mutation was considered LoF. Both metrics did not have to be in agreement to consider a mutation LoF.
8 Get a list of LoF mutations per patient
lof_vep_df <- vep_df_clean %>%
dplyr::filter(Liberal_consequence == "deleterious") %>%
dplyr::mutate(entrez_id = SYMBOLtoENTREZ(SYMBOL)) %>%
dplyr::arrange(sample_id)
list_of_lof_mutations <- split(lof_vep_df, lof_vep_df$sample_id)
# Save the lof_vep_df into a file
lof_vep_out_path <- file.path(results_fip_path,
"lof_mutations.csv")
write.csv(lof_vep_df, file = lof_vep_out_path, row.names = TRUE)
lof_vep_df_reduced <- lof_vep_df %>%
dplyr::rename(hugo_symbol = SYMBOL) %>%
dplyr::select(sample_id, ensembl_id, entrez_id, hugo_symbol, Location, Allele, Liberal_consequence) %>%
dplyr::distinct()
# Save the lof_vep_df_reduced into a file
lof_vep_reduced_out_path <- file.path(results_fip_path,
"lof_mutations_reduced.csv")
write.csv(lof_vep_df_reduced, file = lof_vep_reduced_out_path, row.names = TRUE)9 Calculate TMB per patient
# Step 1: Define the size of the sequenced region (in Mb)
sequenced_region_size_mb <- 25840698/1000000 # Taken from Ref: https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-019-4343-8
# Step 2: Filter for non-synonymous mutations based on literature
tmb_df <- maf_df_annotated %>%
dplyr::filter(Variant_Classification %in% nonsyn_categories) %>%
dplyr::group_by(sample_id) %>%
dplyr::summarise(
total_mutations = n(), # Count mutations for each sample
TMB = total_mutations / sequenced_region_size_mb # Calculate TMB
) %>%
dplyr::mutate(TMB_class = case_when( # Define TMB class based on literature: 10 mut/Mb = High
TMB >= 10 ~ "High",
TMB < 10 ~ "Low")
) %>%
dplyr::arrange(desc(TMB)) # Arrange by TMB in descending order
# View the TMB for each sample/patient
datatable(tmb_df,
extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('copy', 'excel', 'csv'),
scrollX=TRUE,
pageLength=10,
columnDefs = list(list(
targets = "_all",
render = JS(
"function(data, type, row, meta) {",
" return data === null ? 'NA' : data;",
"}"
)
))
),
caption = "Number of mutations and (non-synonymous) TMB per patient"
)10 Plots
10.1 Summary bar plot of Nonsynonymous mutations
# Define output path
nonsyn_plot_path <- file.path(figures_fip_path,
"nonsyn_plot.png")
# Count the occurrences of each unique value in Variant_Classification
top_variants <- nonsyn_maf_df %>%
dplyr::count(Variant_Classification, name = "frequency") %>%
dplyr::mutate(true_frequency = frequency / sum(frequency)) %>%
dplyr::arrange(desc(true_frequency))
# Create a bar plot
nonsyn_plot <- ggplot(top_variants, aes(x = reorder(Variant_Classification,
-true_frequency),
y = true_frequency,
fill = Variant_Classification)) +
geom_bar(stat = "identity") +
scale_fill_brewer(palette = "Set1") + # Use a color palette from RColorBrewer
theme_minimal() +
theme(
panel.background = element_rect(fill = "white", colour = "black"), # Set background to white with grey border
panel.grid.major = element_line(color = "grey", linewidth = 0.5), # Adjust major gridlines
panel.grid.minor = element_blank(), # Hide minor gridlines for a cleaner look
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), # Tilt x-axis labels
plot.background = element_rect(fill = "white", colour = NA) # Set plot background to white
) +
labs(title = "Nonsynonymous mutations' frequency",
x = "Variant Classification",
y = "Relative Frequency") +
scale_y_continuous(breaks = seq(0, 1, by = 0.1)) # Set y-axis breaks every 0.1
# Print plot
print(nonsyn_plot)
# Save plot
ggsave(nonsyn_plot_path, nonsyn_plot, width = 10, height = 8, dpi = 300)10.2 SIFT summary bar plot of LoF mutations
# Define output path
lof_SIFT_plot_path <- file.path(figures_fip_path,
"lof_SIFT_plot.png")
# Count the occurrences of each unique value in Consequence and calculate the true frequency
top_SIFT_lof_variants <- lof_vep_df %>%
dplyr::count(SIFT_cleaned, name = "frequency") %>%
dplyr::mutate(true_frequency = frequency / sum(frequency)) %>%
dplyr::arrange(desc(true_frequency)) %>%
dplyr::slice_max(order_by = true_frequency, n = 5)
# Create a bar plot with the true frequency
lof_SIFT_plot <- ggplot(top_SIFT_lof_variants, aes(x = reorder(SIFT_cleaned,
-true_frequency),
y = true_frequency,
fill = SIFT_cleaned)) +
geom_bar(stat = "identity") +
scale_fill_brewer(palette = "Set1") +
theme_minimal() +
theme(
panel.background = element_rect(fill = "white", colour = "black"),
panel.grid.major = element_line(color = "grey", linewidth = 0.5),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.background = element_rect(fill = "white", colour = NA)
) +
labs(title = "Top 5 Most Frequent SIFT LoF Consequences (Relative Frequency)",
x = "SIFT Consequence",
y = "Relative Frequency") +
scale_y_continuous(breaks = seq(0, 1, by = 0.1)) # Set y-axis breaks every 0.1
# Print plot
print(lof_SIFT_plot)
# Save plot
ggsave(lof_SIFT_plot_path, lof_SIFT_plot, width = 10, height = 8, dpi = 300)10.3 PolyPhen summary bar plot of LoF mutations
# Define output path
lof_PolyPhen_plot_path <- file.path(figures_fip_path,
".lof_PolyPhen_plot.png")
# Count the occurrences of each unique value in Consequence and calculate the true frequency
top_PolyPhen_lof_variants <- lof_vep_df %>%
dplyr::count(PolyPhen_cleaned, name = "frequency") %>%
dplyr::mutate(true_frequency = frequency / sum(frequency)) %>%
dplyr::arrange(desc(true_frequency)) %>%
dplyr::slice_max(order_by = true_frequency, n = 5)
# Create a bar plot with the true frequency
lof_PolyPhen_plot <- ggplot(top_PolyPhen_lof_variants, aes(x = reorder(PolyPhen_cleaned,
-true_frequency),
y = true_frequency,
fill = PolyPhen_cleaned)) +
geom_bar(stat = "identity") +
scale_fill_brewer(palette = "Set1") +
theme_minimal() +
theme(
panel.background = element_rect(fill = "white", colour = "black"),
panel.grid.major = element_line(color = "grey", linewidth = 0.5),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
plot.background = element_rect(fill = "white", colour = NA)
) +
labs(title = "Top 5 Most Frequent PolyPhen LoF Consequences (Relative Frequency)",
x = "PolyPhen Consequence",
y = "Relative Frequency") +
scale_y_continuous(breaks = seq(0, 1, by = 0.1)) # Set y-axis breaks every 0.1
# Print plot
print(lof_PolyPhen_plot)
# Save plot
ggsave(lof_PolyPhen_plot_path, lof_PolyPhen_plot, width = 10, height = 8, dpi = 300)10.4 Box plot of TMB grouped by response
#Fig2a
# Merge sup1_response with tmb_df
tmb_response_df <- tmb_df %>%
left_join(sup1_response_df %>%
dplyr::select(Sample.ID, Patient.Response),
by = c("sample_id" = "Sample.ID")
) %>%
dplyr::select(sample_id,
TMB,
patient_response = Patient.Response) %>%
dplyr::filter(!(sample_id %in% c("MM909_20", "MM909_24")))
# Perform the Mann-Whitney test and extract p-value
test_result <- compare_means(TMB ~ patient_response, data = tmb_response_df, method = "wilcox.test", p.adjust.method = "none")
# Print the test result to see the p-value
print(test_result)## # A tibble: 1 × 8
## .y. group1 group2 p p.adj p.format p.signif method
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 TMB R NR 0.0249 0.025 0.025 * Wilcoxon# Define output path
tmb_boxplot_path <- file.path(figures_fip_path,
"tmb_boxplot.png")
# Create a boxplot with medians and p-value displayed
tmb_boxplot <- ggplot(tmb_response_df, aes(x = patient_response,
y = TMB,
fill = patient_response)) +
geom_boxplot() +
stat_summary(fun = median, geom = "text", aes(label = round(..y.., 2)),
position = position_nudge(x = 0, y = 1.5), size = 4, color = "white") +
stat_compare_means(method = "wilcox.test", label = "p.format", label.x = 1.5) + # Add p-value from Mann-Whitney test
scale_fill_manual(values = c(R = "#009E73", NR = "#D55E00"),
labels = c("R" = "Responders", "NR" = "Non-responders")) +
scale_x_discrete(labels = c("NR" = "Non-responders",
"R" = "Responders")) + # Manually set x-axis labels
theme_minimal() +
theme(
panel.background = element_rect(fill = "white", colour = "black"),
panel.grid.major = element_line(color = "grey", linewidth = 0.5),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white", colour = NA)
) +
labs(title = NULL,
x = "Patient Response",
y = "TMB (mutations/Mb)",
fill = "Patient Response")
# Print plot
print(tmb_boxplot)
# Save plot
ggsave(tmb_boxplot_path, tmb_boxplot, width = 8, height = 6, dpi = 300)10.5 Box plot of LoF mutations grouped by response
#Fig5
# Merge sup1_response with lof_vep_df_reduced
lof_response_df <- lof_vep_df_reduced %>%
left_join(sup1_response_df %>%
dplyr::select(Sample.ID, Patient.Response),
by = c("sample_id" = "Sample.ID")
) %>%
dplyr::filter(!(sample_id %in% c("MM909_20", "MM909_24"))) %>%
dplyr::rename(patient_response = Patient.Response)
# Calculate the sum of mutations for each sample
lof_counts <- lof_response_df %>%
group_by(sample_id) %>%
summarise(lof_count = n()) %>%
ungroup()
# Join the lof_count back to the original data frame
lof_count_response_df <- lof_response_df %>%
left_join(lof_counts, by = "sample_id") %>%
dplyr::select(sample_id, patient_response, lof_count) %>%
dplyr::distinct()
# Perform the Mann-Whitney test and extract p-value
test_result <- compare_means(lof_count ~ patient_response, data = lof_count_response_df, method = "wilcox.test", p.adjust.method = "none")
# Print the test result to see the p-value
print(test_result)## # A tibble: 1 × 8
## .y. group1 group2 p p.adj p.format p.signif method
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 lof_count NR R 0.0206 0.021 0.021 * Wilcoxon# Define output path
lof_boxplot_path <- file.path(figures_fip_path,
"lof_boxplot.png")
# Create a boxplot with medians and p-value displayed
lof_boxplot <- ggplot(lof_count_response_df, aes(x = patient_response,
y = lof_count,
fill = patient_response)) +
geom_boxplot() +
stat_summary(fun = median, geom = "text", aes(label = round(..y.., 2)),
position = position_nudge(x = 0, y = 18), size = 4, color = "white") +
stat_compare_means(method = "wilcox.test", label = "p.format", label.x = 1.5) + # Add p-value from Mann-Whitney test
scale_fill_manual(values = c(R = "#009E73", NR = "#D55E00"),
labels = c("R" = "Responders", "NR" = "Non-responders")) +
scale_x_discrete(labels = c("NR" = "Non-responders",
"R" = "Responders")) + # Manually set x-axis labels
theme_minimal() +
theme(
panel.background = element_rect(fill = "white", colour = "black"),
panel.grid.major = element_line(color = "grey", linewidth = 0.5),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white", colour = NA)
) +
labs(title = NULL,
x = "Patient Response",
y = "nº LoF mutations",
fill = "Patient Response")
# Print plot
print(lof_boxplot)
# Save plot
ggsave(lof_boxplot_path, lof_boxplot, width = 8, height = 6, dpi = 300)11 MAFtools
11.1 All mutations
11.1.1 Load data
# Define paths
maf_data <- "41467_2017_1460_MOESM6_ESM_somatic_mutations.xlsx"
maf_path <- file.path(data_path,
maf_data)
clinical_data <- "41467_2017_1460_MOESM4_ESM_clinical.xlsx"
clinical_path <- file.path(data_path,
clinical_data)
# Read data
maf_df <- read_excel(maf_path,
skip=1,
col_names=TRUE) %>%
mutate(sample_id = sub("_[12]$", "", tumor_name)) %>%
dplyr::select(sample_id, everything()) %>%
arrange(sample_id) %>%
dplyr::filter(!(sample_id %in% c("MM909_20", "MM909_24")))
clinical_df <- read_excel(clinical_path,
skip=1,
col_names=TRUE)
colnames(clinical_df)[1] <- "sample_id"
# Add Tumor_Sample_Barcode to clinical_df
mapping_df <- maf_df_annotated %>%
dplyr::select(sample_id, Tumor_Sample_Barcode) %>%
distinct()
# Clean sup1_response_df
clinical_resp_df <- sup1_response_df %>%
dplyr::rename(sample_id = Sample.ID,
Patient_Response = Patient.Response) %>%
dplyr::mutate(
Patient_Response = case_when(
Patient_Response == "R" ~ "Responders",
Patient_Response == "NR" ~ "Non-responders"
)
)
clinical_resp_df <- clinical_resp_df %>%
left_join(mapping_df, by = "sample_id") %>%
dplyr::rename(Sample_ID = sample_id)
maf_df_annotated <- maf_df_annotated %>%
dplyr::rename(Sample_ID = sample_id)
# Load maf with clinical data
mm909 = read.maf(maf = maf_df_annotated, clinicalData = clinical_resp_df)## -Validating
## -Silent variants: 3778
## -Summarizing
## --Possible FLAGS among top ten genes:
## MUC16
## TTN
## -Processing clinical data
## -Finished in 1.499s elapsed (0.935s cpu)11.1.2 Summaries
## Shows sample summry.
sample_sum <- getSampleSummary(mm909)
getSampleSummary(mm909)## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Nonstop_Mutation
## <fctr> <int> <int> <int>
## 1: MM909_22_1 1271 65 1
## 2: MM909_03_1 1159 67 0
## 3: MM909_26_1 828 47 0
## 4: MM909_42_1 398 24 0
## 5: MM909_43_1 349 27 0
## 6: MM909_15_2 342 21 0
## 7: MM909_46_1 287 22 0
## 8: MM909_16_1 285 19 0
## 9: MM909_17_1 237 8 1
## 10: MM909_25_1 168 12 0
## 11: MM909_11_1 155 14 0
## 12: MM909_31_1 150 12 0
## 13: MM909_40_1 138 9 1
## 14: MM909_02_1 111 5 0
## 15: MM909_35_1 101 3 0
## 16: MM909_34_2 87 9 0
## 17: MM909_37_1 76 5 0
## 18: MM909_27_1 68 2 1
## 19: MM909_06_1 46 4 0
## 20: MM909_36_1 45 4 0
## 21: MM909_14_1 29 2 0
## 22: MM909_47_1 15 0 0
## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Nonstop_Mutation
## Splice_Site total
## <int> <num>
## 1: 52 1389
## 2: 59 1285
## 3: 28 903
## 4: 14 436
## 5: 16 392
## 6: 13 376
## 7: 10 319
## 8: 11 315
## 9: 19 265
## 10: 6 186
## 11: 9 178
## 12: 11 173
## 13: 8 156
## 14: 9 125
## 15: 3 107
## 16: 4 100
## 17: 2 83
## 18: 2 73
## 19: 2 52
## 20: 1 50
## 21: 4 35
## 22: 2 17
## Splice_Site total## Shows gene summary.
getGeneSummary(mm909)## Hugo_Symbol Missense_Mutation Nonsense_Mutation Nonstop_Mutation
## <char> <int> <int> <int>
## 1: BRAF 16 0 0
## 2: MIR548N 25 5 0
## 3: MUC16 40 1 0
## 4: TTN 30 1 0
## 5: PCLO 14 1 0
## ---
## 4623: ZPLD1 1 0 0
## 4624: ZSCAN18 1 0 0
## 4625: ZSCAN31 1 0 0
## 4626: ZSWIM5 1 0 0
## 4627: ZWILCH 1 0 0
## Splice_Site total MutatedSamples AlteredSamples
## <int> <num> <int> <int>
## 1: 0 16 14 14
## 2: 0 30 13 13
## 3: 0 41 12 12
## 4: 0 31 11 11
## 5: 0 15 9 9
## ---
## 4623: 0 1 1 1
## 4624: 0 1 1 1
## 4625: 0 1 1 1
## 4626: 0 1 1 1
## 4627: 0 1 1 1## Shows clinical data associated with samples
getClinicalData(mm909)## Sample_ID Sample.Timepoint AJCC.Stage PFS.Time PFS.Event
## <char> <char> <char> <char> <char>
## 1: MM909_02 Before TILs M1b 2.00 1
## 2: MM909_03 Before TILs M1c 3.80 1
## 3: MM909_06 Before TILs M1c 2.30 1
## 4: MM909_11 Before TILs M1a 13.10 1
## 5: MM909_14 Before TILs M1c 2.50 1
## 6: MM909_15 Before TILs M1b 67.13 0
## 7: MM909_16 Before TILs M1c 3.90 1
## 8: MM909_17 Before TILs M1c 64.87 0
## 9: MM909_22 After Ipilimumab/Before TILs M1c 32.10 0
## 10: MM909_25 Before TILs M1b 3.77 1
## 11: MM909_26 Before TILs M1c 52.70 0
## 12: MM909_27 Before Vemurafenib/Before TILs M1c 2.00 1
## 13: MM909_31 Before TILs M1c 11.30 1
## 14: MM909_34 Before TILs M1c 2.83 1
## 15: MM909_35 During Vemurafenib/Before TILs M1c 3.13 1
## 16: MM909_36 During Vemurafenib/Before TILs M1c 8.23 1
## 17: MM909_37 Before TILs M1c 3.03 1
## 18: MM909_40 Before TILs M1c 3.73 1
## 19: MM909_42 Before TILs M1c 42.30 0
## 20: MM909_43 Before TILs M1c 1.93 1
## 21: MM909_46 Before TILs M1c 5.83 1
## 22: MM909_47 Before TILs M1c 3.27 1
## Sample_ID Sample.Timepoint AJCC.Stage PFS.Time PFS.Event
## OS.Time OS.Event Type.of.Lesion Type.of.Primary RECIST Patient_Response
## <char> <char> <char> <char> <char> <char>
## 1: 6.9 1 LN skin PD Non-responders
## 2: 11.4 1 SC unknown SD Non-responders
## 3: 4.6 1 LN skin PD Non-responders
## 4: 71.5 0 LN skin CR Responders
## 5: 3.2 1 LN unknown PD Non-responders
## 6: 67.1 0 LN skin CR Responders
## 7: 65.3 0 SC skin SD Responders
## 8: 64.9 0 LN skin PR Responders
## 9: 32.1 1 <NA> skin PR Responders
## 10: 5.5 1 LN skin SD Non-responders
## 11: 52.7 0 IM skin CR Responders
## 12: 3.5 1 SC skin PD Non-responders
## 13: 20.1 1 SC mucosa PR Responders
## 14: 5.0 1 LN mucosa SD Non-responders
## 15: 23.7 1 SC skin SD Non-responders
## 16: 24.6 1 IA skin PR Responders
## 17: 12.1 1 SC skin SD Non-responders
## 18: 14.0 1 IA skin SD Non-responders
## 19: 42.3 0 SC skin CR Responders
## 20: 22.8 1 SC unknown PD Non-responders
## 21: 24.8 1 LN skin SD Responders
## 22: 18.9 1 pleura unknown SD Non-responders
## OS.Time OS.Event Type.of.Lesion Type.of.Primary RECIST Patient_Response
## mut_load Tumor_Sample_Barcode
## <char> <char>
## 1: 199 MM909_02_1
## 2: 2036 MM909_03_1
## 3: 86 MM909_06_1
## 4: 277 MM909_11_1
## 5: 62 MM909_14_1
## 6: 581 MM909_15_2
## 7: 493 MM909_16_1
## 8: 366 MM909_17_1
## 9: 2200 MM909_22_1
## 10: 295 MM909_25_1
## 11: 1378 MM909_26_1
## 12: 110 MM909_27_1
## 13: 246 MM909_31_1
## 14: 157 MM909_34_2
## 15: 180 MM909_35_1
## 16: 85 MM909_36_1
## 17: 131 MM909_37_1
## 18: 214 MM909_40_1
## 19: 654 MM909_42_1
## 20: 601 MM909_43_1
## 21: 488 MM909_46_1
## 22: 23 MM909_47_1
## mut_load Tumor_Sample_Barcode## Shows all fields in MAF
getFields(mm909)## [1] "Sample_ID" "ensembl_id" "entrez_id"
## [4] "Hugo_Symbol" "contig" "position"
## [7] "context" "ref_allele" "alt_allele"
## [10] "tumor_name" "normal_name" "score"
## [13] "dbsnp_site" "covered" "power"
## [16] "tumor_power" "normal_power" "total_pairs"
## [19] "improper_pairs" "map_Q0_reads" "t_lod_fstar"
## [22] "tumor_f" "contaminant_fraction" "contaminant_lod"
## [25] "t_ref_count" "t_alt_count" "t_ref_sum"
## [28] "t_alt_sum" "t_ref_max_mapq" "t_alt_max_mapq"
## [31] "t_ins_count" "t_del_count" "normal_best_gt"
## [34] "init_n_lod" "n_ref_count" "n_alt_count"
## [37] "n_ref_sum" "n_alt_sum" "judgement"
## [40] "chrom23" "Chromosome" "Start_Position"
## [43] "End_Position" "Strand" "Tumor_Sample_Barcode"
## [46] "Center" "NCBI_Build" "Variant_Classification"
## [49] "Variant_Type" "Reference_Allele" "Tumor_Seq_Allele1"
## [52] "Tumor_Seq_Allele2" "cDNA_Change" "Codon_Change"
## [55] "Protein_Change"## Shows clinical enrichment
clinicalEnrichment(mm909, "Patient_Response")##
## Non-responders Responders
## 12 10
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## <char> <char> <char> <char> <char>
## 1: BRAF Responders Non-responders 6 of 10 8 of 12
## 2: BRAF <NA> <NA> <NA> <NA>
## 3: BRAF <NA> <NA> <NA> <NA>
## 4: MIR548N Responders Non-responders 8 of 10 5 of 12
## 5: MIR548N <NA> <NA> <NA> <NA>
## 6: MIR548N <NA> <NA> <NA> <NA>
## 7: MUC16 Responders Non-responders 8 of 10 4 of 12
## 8: MUC16 <NA> <NA> <NA> <NA>
## 9: MUC16 <NA> <NA> <NA> <NA>
## 10: TTN Responders Non-responders 5 of 10 6 of 12
## 11: TTN <NA> <NA> <NA> <NA>
## 12: TTN <NA> <NA> <NA> <NA>
## 13: PCLO Responders Non-responders 4 of 10 5 of 12
## 14: PCLO <NA> <NA> <NA> <NA>
## 15: PCLO <NA> <NA> <NA> <NA>
## 16: DNAH5 Responders Non-responders 5 of 10 4 of 12
## 17: DNAH5 <NA> <NA> <NA> <NA>
## 18: DNAH5 <NA> <NA> <NA> <NA>
## 19: LRP1B Responders Non-responders 5 of 10 2 of 12
## 20: LRP1B <NA> <NA> <NA> <NA>
## 21: LRP1B <NA> <NA> <NA> <NA>
## 22: FSIP2 Responders Non-responders 4 of 10 3 of 12
## 23: FSIP2 <NA> <NA> <NA> <NA>
## 24: FSIP2 <NA> <NA> <NA> <NA>
## 25: PTPRT Responders Non-responders 4 of 10 3 of 12
## 26: PTPRT <NA> <NA> <NA> <NA>
## 27: PTPRT <NA> <NA> <NA> <NA>
## 28: CSMD1 Responders Non-responders 3 of 10 4 of 12
## 29: CSMD1 <NA> <NA> <NA> <NA>
## 30: CSMD1 <NA> <NA> <NA> <NA>
## 31: USH2A Responders Non-responders 5 of 10 2 of 12
## 32: USH2A <NA> <NA> <NA> <NA>
## 33: USH2A <NA> <NA> <NA> <NA>
## 34: SCN10A Responders Non-responders 4 of 10 3 of 12
## 35: SCN10A <NA> <NA> <NA> <NA>
## 36: SCN10A <NA> <NA> <NA> <NA>
## 37: HYDIN Responders Non-responders 5 of 10 2 of 12
## 38: HYDIN <NA> <NA> <NA> <NA>
## 39: HYDIN <NA> <NA> <NA> <NA>
## 40: ZIM2 Responders Non-responders 5 of 10 2 of 12
## 41: ZIM2 <NA> <NA> <NA> <NA>
## 42: ZIM2 <NA> <NA> <NA> <NA>
## 43: ABCA13 Responders Non-responders 3 of 10 3 of 12
## 44: ABCA13 <NA> <NA> <NA> <NA>
## 45: ABCA13 <NA> <NA> <NA> <NA>
## 46: DNAH8 Responders Non-responders 4 of 10 2 of 12
## 47: DNAH8 <NA> <NA> <NA> <NA>
## 48: DNAH8 <NA> <NA> <NA> <NA>
## 49: FAM83B Responders Non-responders 4 of 10 2 of 12
## 50: FAM83B <NA> <NA> <NA> <NA>
## 51: FAM83B <NA> <NA> <NA> <NA>
## 52: DSCAM Responders Non-responders 3 of 10 3 of 12
## 53: DSCAM <NA> <NA> <NA> <NA>
## 54: DSCAM <NA> <NA> <NA> <NA>
## 55: MYO15A Responders Non-responders 3 of 10 3 of 12
## 56: MYO15A <NA> <NA> <NA> <NA>
## 57: MYO15A <NA> <NA> <NA> <NA>
## 58: DNAH6 Responders Non-responders 3 of 10 3 of 12
## 59: DNAH6 <NA> <NA> <NA> <NA>
## 60: DNAH6 <NA> <NA> <NA> <NA>
## 61: WDFY4 Responders Non-responders 4 of 10 2 of 12
## 62: WDFY4 <NA> <NA> <NA> <NA>
## 63: WDFY4 <NA> <NA> <NA> <NA>
## 64: COL4A5 Responders Non-responders 4 of 10 2 of 12
## 65: COL4A5 <NA> <NA> <NA> <NA>
## 66: COL4A5 <NA> <NA> <NA> <NA>
## 67: FAT4 Responders Non-responders 4 of 10 2 of 12
## 68: FAT4 <NA> <NA> <NA> <NA>
## 69: FAT4 <NA> <NA> <NA> <NA>
## 70: MYH8 Responders Non-responders 5 of 10 1 of 12
## 71: MYH8 <NA> <NA> <NA> <NA>
## 72: MYH8 <NA> <NA> <NA> <NA>
## 73: PKHD1 Responders Non-responders 5 of 10 1 of 12
## 74: PKHD1 <NA> <NA> <NA> <NA>
## 75: PKHD1 <NA> <NA> <NA> <NA>
## 76: CNTN5 Responders Non-responders 3 of 10 3 of 12
## 77: CNTN5 <NA> <NA> <NA> <NA>
## 78: CNTN5 <NA> <NA> <NA> <NA>
## 79: EPHA6 Responders Non-responders 5 of 10 1 of 12
## 80: EPHA6 <NA> <NA> <NA> <NA>
## 81: EPHA6 <NA> <NA> <NA> <NA>
## 82: SHANK1 Responders Non-responders 2 of 10 4 of 12
## 83: SHANK1 <NA> <NA> <NA> <NA>
## 84: SHANK1 <NA> <NA> <NA> <NA>
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## fdr Analysis Group1 Group2 n_mutated_group1 n_mutated_group2
## <num> <char> <char> <char> <char> <char>
## 1: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 2: NA Group Non-responders Rest 8 of 12 6 of 10
## 3: NA Group Responders Rest 6 of 10 8 of 12
## 4: 0.09907121 Pairwise <NA> <NA> <NA> <NA>
## 5: NA Group Non-responders Rest 5 of 12 8 of 10
## 6: NA Group Responders Rest 8 of 10 5 of 12
## 7: 0.04273126 Pairwise <NA> <NA> <NA> <NA>
## 8: NA Group Non-responders Rest 4 of 12 8 of 10
## 9: NA Group Responders Rest 8 of 10 4 of 12
## 10: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 11: NA Group Non-responders Rest 6 of 12 5 of 10
## 12: NA Group Responders Rest 5 of 10 6 of 12
## 13: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 14: NA Group Non-responders Rest 5 of 12 4 of 10
## 15: NA Group Responders Rest 4 of 10 5 of 12
## 16: 0.66563467 Pairwise <NA> <NA> <NA> <NA>
## 17: NA Group Non-responders Rest 4 of 12 5 of 10
## 18: NA Group Responders Rest 5 of 10 4 of 12
## 19: 0.17182663 Pairwise <NA> <NA> <NA> <NA>
## 20: NA Group Non-responders Rest 2 of 12 5 of 10
## 21: NA Group Responders Rest 5 of 10 2 of 12
## 22: 0.65170279 Pairwise <NA> <NA> <NA> <NA>
## 23: NA Group Non-responders Rest 3 of 12 4 of 10
## 24: NA Group Responders Rest 4 of 10 3 of 12
## 25: 0.65170279 Pairwise <NA> <NA> <NA> <NA>
## 26: NA Group Non-responders Rest 3 of 12 4 of 10
## 27: NA Group Responders Rest 4 of 10 3 of 12
## 28: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 29: NA Group Non-responders Rest 4 of 12 3 of 10
## 30: NA Group Responders Rest 3 of 10 4 of 12
## 31: 0.17182663 Pairwise <NA> <NA> <NA> <NA>
## 32: NA Group Non-responders Rest 2 of 12 5 of 10
## 33: NA Group Responders Rest 5 of 10 2 of 12
## 34: 0.65170279 Pairwise <NA> <NA> <NA> <NA>
## 35: NA Group Non-responders Rest 3 of 12 4 of 10
## 36: NA Group Responders Rest 4 of 10 3 of 12
## 37: 0.17182663 Pairwise <NA> <NA> <NA> <NA>
## 38: NA Group Non-responders Rest 2 of 12 5 of 10
## 39: NA Group Responders Rest 5 of 10 2 of 12
## 40: 0.17182663 Pairwise <NA> <NA> <NA> <NA>
## 41: NA Group Non-responders Rest 2 of 12 5 of 10
## 42: NA Group Responders Rest 5 of 10 2 of 12
## 43: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 44: NA Group Non-responders Rest 3 of 12 3 of 10
## 45: NA Group Responders Rest 3 of 10 3 of 12
## 46: 0.34763379 Pairwise <NA> <NA> <NA> <NA>
## 47: NA Group Non-responders Rest 2 of 12 4 of 10
## 48: NA Group Responders Rest 4 of 10 2 of 12
## 49: 0.34763379 Pairwise <NA> <NA> <NA> <NA>
## 50: NA Group Non-responders Rest 2 of 12 4 of 10
## 51: NA Group Responders Rest 4 of 10 2 of 12
## 52: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 53: NA Group Non-responders Rest 3 of 12 3 of 10
## 54: NA Group Responders Rest 3 of 10 3 of 12
## 55: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 56: NA Group Non-responders Rest 3 of 12 3 of 10
## 57: NA Group Responders Rest 3 of 10 3 of 12
## 58: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 59: NA Group Non-responders Rest 3 of 12 3 of 10
## 60: NA Group Responders Rest 3 of 10 3 of 12
## 61: 0.34763379 Pairwise <NA> <NA> <NA> <NA>
## 62: NA Group Non-responders Rest 2 of 12 4 of 10
## 63: NA Group Responders Rest 4 of 10 2 of 12
## 64: 0.34763379 Pairwise <NA> <NA> <NA> <NA>
## 65: NA Group Non-responders Rest 2 of 12 4 of 10
## 66: NA Group Responders Rest 4 of 10 2 of 12
## 67: 0.34763379 Pairwise <NA> <NA> <NA> <NA>
## 68: NA Group Non-responders Rest 2 of 12 4 of 10
## 69: NA Group Responders Rest 4 of 10 2 of 12
## 70: 0.05572755 Pairwise <NA> <NA> <NA> <NA>
## 71: NA Group Non-responders Rest 1 of 12 5 of 10
## 72: NA Group Responders Rest 5 of 10 1 of 12
## 73: 0.05572755 Pairwise <NA> <NA> <NA> <NA>
## 74: NA Group Non-responders Rest 1 of 12 5 of 10
## 75: NA Group Responders Rest 5 of 10 1 of 12
## 76: 1.00000000 Pairwise <NA> <NA> <NA> <NA>
## 77: NA Group Non-responders Rest 3 of 12 3 of 10
## 78: NA Group Responders Rest 3 of 10 3 of 12
## 79: 0.05572755 Pairwise <NA> <NA> <NA> <NA>
## 80: NA Group Non-responders Rest 1 of 12 5 of 10
## 81: NA Group Responders Rest 5 of 10 1 of 12
## 82: 0.64617426 Pairwise <NA> <NA> <NA> <NA>
## 83: NA Group Non-responders Rest 4 of 12 2 of 10
## 84: NA Group Responders Rest 2 of 10 4 of 12
## fdr Analysis Group1 Group2 n_mutated_group1 n_mutated_group2
## p_value OR OR_low OR_high
## <num> <num> <num> <num>
## 1: NA NA NA NA
## 2: 1.00000000 1.3158848 0.166513699 10.588251
## 3: 1.00000000 0.7599449 0.094444307 6.005512
## 4: NA NA NA NA
## 5: 0.09907121 0.1943170 0.014105470 1.601714
## 6: 0.09907121 5.1462289 0.624331001 70.894480
## 7: NA NA NA NA
## 8: 0.04273126 0.1393973 0.009829588 1.164437
## 9: 0.04273126 7.1737384 0.858784223 101.733669
## 10: NA NA NA NA
## 11: 1.00000000 1.0000000 0.138668627 7.211437
## 12: 1.00000000 1.0000000 0.138668627 7.211437
## 13: NA NA NA NA
## 14: 1.00000000 1.0680540 0.144162138 8.236116
## 15: 1.00000000 0.9362822 0.121416450 6.936634
## 16: NA NA NA NA
## 17: 0.66563467 0.5163899 0.064087475 3.791432
## 18: 0.66563467 1.9365213 0.263752588 15.603673
## 19: NA NA NA NA
## 20: 0.17182663 0.2166050 0.015292506 1.914989
## 21: 0.17182663 4.6166977 0.522196276 65.391505
## 22: NA NA NA NA
## 23: 0.65170279 0.5163825 0.054045583 4.332564
## 24: 0.65170279 1.9365490 0.230810220 18.502900
## 25: NA NA NA NA
## 26: 0.65170279 0.5163825 0.054045583 4.332564
## 27: 0.65170279 1.9365490 0.230810220 18.502900
## 28: NA NA NA NA
## 29: 1.00000000 1.1585362 0.137618146 10.854999
## 30: 1.00000000 0.8631582 0.092123452 7.266484
## 31: NA NA NA NA
## 32: 0.17182663 0.2166050 0.015292506 1.914989
## 33: 0.17182663 4.6166977 0.522196276 65.391505
## 34: NA NA NA NA
## 35: 0.65170279 0.5163825 0.054045583 4.332564
## 36: 0.65170279 1.9365490 0.230810220 18.502900
## 37: NA NA NA NA
## 38: 0.17182663 0.2166050 0.015292506 1.914989
## 39: 0.17182663 4.6166977 0.522196276 65.391505
## 40: NA NA NA NA
## 41: 0.17182663 0.2166050 0.015292506 1.914989
## 42: 0.17182663 4.6166977 0.522196276 65.391505
## 43: NA NA NA NA
## 44: 1.00000000 0.7867773 0.078561110 7.821058
## 45: 1.00000000 1.2710078 0.127859942 12.728944
## 46: NA NA NA NA
## 47: 0.34763379 0.3176677 0.022167729 3.025373
## 48: 0.34763379 3.1479440 0.330537773 45.110621
## 49: NA NA NA NA
## 50: 0.34763379 0.3176677 0.022167729 3.025373
## 51: 0.34763379 3.1479440 0.330537773 45.110621
## 52: NA NA NA NA
## 53: 1.00000000 0.7867773 0.078561110 7.821058
## 54: 1.00000000 1.2710078 0.127859942 12.728944
## 55: NA NA NA NA
## 56: 1.00000000 0.7867773 0.078561110 7.821058
## 57: 1.00000000 1.2710078 0.127859942 12.728944
## 58: NA NA NA NA
## 59: 1.00000000 0.7867773 0.078561110 7.821058
## 60: 1.00000000 1.2710078 0.127859942 12.728944
## 61: NA NA NA NA
## 62: 0.34763379 0.3176677 0.022167729 3.025373
## 63: 0.34763379 3.1479440 0.330537773 45.110621
## 64: NA NA NA NA
## 65: 0.34763379 0.3176677 0.022167729 3.025373
## 66: 0.34763379 3.1479440 0.330537773 45.110621
## 67: NA NA NA NA
## 68: 0.34763379 0.3176677 0.022167729 3.025373
## 69: 0.34763379 3.1479440 0.330537773 45.110621
## 70: NA NA NA NA
## 71: 0.05572755 0.1024623 0.001773299 1.250950
## 72: 0.05572755 9.7596867 0.799392236 563.920542
## 73: NA NA NA NA
## 74: 0.05572755 0.1024623 0.001773299 1.250950
## 75: 0.05572755 9.7596867 0.799392236 563.920542
## 76: NA NA NA NA
## 77: 1.00000000 0.7867773 0.078561110 7.821058
## 78: 1.00000000 1.2710078 0.127859942 12.728944
## 79: NA NA NA NA
## 80: 0.05572755 0.1024623 0.001773299 1.250950
## 81: 0.05572755 9.7596867 0.799392236 563.920542
## 82: NA NA NA NA
## 83: 0.64617426 1.9386158 0.204565986 27.339825
## 84: 0.64617426 0.5158320 0.036576679 4.888398
## p_value OR OR_low OR_high## $pairwise_comparision
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## <char> <char> <char> <char> <char>
## 1: MUC16 Responders Non-responders 8 of 10 4 of 12
## 2: MYH8 Responders Non-responders 5 of 10 1 of 12
## 3: PKHD1 Responders Non-responders 5 of 10 1 of 12
## 4: EPHA6 Responders Non-responders 5 of 10 1 of 12
## 5: MIR548N Responders Non-responders 8 of 10 5 of 12
## 6: LRP1B Responders Non-responders 5 of 10 2 of 12
## 7: USH2A Responders Non-responders 5 of 10 2 of 12
## 8: HYDIN Responders Non-responders 5 of 10 2 of 12
## 9: ZIM2 Responders Non-responders 5 of 10 2 of 12
## 10: DNAH8 Responders Non-responders 4 of 10 2 of 12
## 11: FAM83B Responders Non-responders 4 of 10 2 of 12
## 12: WDFY4 Responders Non-responders 4 of 10 2 of 12
## 13: COL4A5 Responders Non-responders 4 of 10 2 of 12
## 14: FAT4 Responders Non-responders 4 of 10 2 of 12
## 15: SHANK1 Responders Non-responders 2 of 10 4 of 12
## 16: FSIP2 Responders Non-responders 4 of 10 3 of 12
## 17: PTPRT Responders Non-responders 4 of 10 3 of 12
## 18: SCN10A Responders Non-responders 4 of 10 3 of 12
## 19: DNAH5 Responders Non-responders 5 of 10 4 of 12
## 20: TTN Responders Non-responders 5 of 10 6 of 12
## 21: CSMD1 Responders Non-responders 3 of 10 4 of 12
## 22: ABCA13 Responders Non-responders 3 of 10 3 of 12
## 23: DSCAM Responders Non-responders 3 of 10 3 of 12
## 24: MYO15A Responders Non-responders 3 of 10 3 of 12
## 25: DNAH6 Responders Non-responders 3 of 10 3 of 12
## 26: CNTN5 Responders Non-responders 3 of 10 3 of 12
## 27: BRAF Responders Non-responders 6 of 10 8 of 12
## 28: PCLO Responders Non-responders 4 of 10 5 of 12
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## fdr
## <num>
## 1: 0.04273126
## 2: 0.05572755
## 3: 0.05572755
## 4: 0.05572755
## 5: 0.09907121
## 6: 0.17182663
## 7: 0.17182663
## 8: 0.17182663
## 9: 0.17182663
## 10: 0.34763379
## 11: 0.34763379
## 12: 0.34763379
## 13: 0.34763379
## 14: 0.34763379
## 15: 0.64617426
## 16: 0.65170279
## 17: 0.65170279
## 18: 0.65170279
## 19: 0.66563467
## 20: 1.00000000
## 21: 1.00000000
## 22: 1.00000000
## 23: 1.00000000
## 24: 1.00000000
## 25: 1.00000000
## 26: 1.00000000
## 27: 1.00000000
## 28: 1.00000000
## fdr
##
## $groupwise_comparision
## Hugo_Symbol Group1 Group2 n_mutated_group1 n_mutated_group2
## <char> <char> <char> <char> <char>
## 1: MUC16 Non-responders Rest 4 of 12 8 of 10
## 2: MUC16 Responders Rest 8 of 10 4 of 12
## 3: MYH8 Non-responders Rest 1 of 12 5 of 10
## 4: MYH8 Responders Rest 5 of 10 1 of 12
## 5: PKHD1 Non-responders Rest 1 of 12 5 of 10
## 6: PKHD1 Responders Rest 5 of 10 1 of 12
## 7: EPHA6 Non-responders Rest 1 of 12 5 of 10
## 8: EPHA6 Responders Rest 5 of 10 1 of 12
## 9: MIR548N Non-responders Rest 5 of 12 8 of 10
## 10: MIR548N Responders Rest 8 of 10 5 of 12
## 11: LRP1B Non-responders Rest 2 of 12 5 of 10
## 12: LRP1B Responders Rest 5 of 10 2 of 12
## 13: USH2A Non-responders Rest 2 of 12 5 of 10
## 14: USH2A Responders Rest 5 of 10 2 of 12
## 15: HYDIN Non-responders Rest 2 of 12 5 of 10
## 16: HYDIN Responders Rest 5 of 10 2 of 12
## 17: ZIM2 Non-responders Rest 2 of 12 5 of 10
## 18: ZIM2 Responders Rest 5 of 10 2 of 12
## 19: DNAH8 Non-responders Rest 2 of 12 4 of 10
## 20: DNAH8 Responders Rest 4 of 10 2 of 12
## 21: FAM83B Non-responders Rest 2 of 12 4 of 10
## 22: FAM83B Responders Rest 4 of 10 2 of 12
## 23: WDFY4 Non-responders Rest 2 of 12 4 of 10
## 24: WDFY4 Responders Rest 4 of 10 2 of 12
## 25: COL4A5 Non-responders Rest 2 of 12 4 of 10
## 26: COL4A5 Responders Rest 4 of 10 2 of 12
## 27: FAT4 Non-responders Rest 2 of 12 4 of 10
## 28: FAT4 Responders Rest 4 of 10 2 of 12
## 29: SHANK1 Non-responders Rest 4 of 12 2 of 10
## 30: SHANK1 Responders Rest 2 of 10 4 of 12
## 31: FSIP2 Non-responders Rest 3 of 12 4 of 10
## 32: FSIP2 Responders Rest 4 of 10 3 of 12
## 33: PTPRT Non-responders Rest 3 of 12 4 of 10
## 34: PTPRT Responders Rest 4 of 10 3 of 12
## 35: SCN10A Non-responders Rest 3 of 12 4 of 10
## 36: SCN10A Responders Rest 4 of 10 3 of 12
## 37: DNAH5 Non-responders Rest 4 of 12 5 of 10
## 38: DNAH5 Responders Rest 5 of 10 4 of 12
## 39: TTN Non-responders Rest 6 of 12 5 of 10
## 40: TTN Responders Rest 5 of 10 6 of 12
## 41: CSMD1 Non-responders Rest 4 of 12 3 of 10
## 42: CSMD1 Responders Rest 3 of 10 4 of 12
## 43: ABCA13 Non-responders Rest 3 of 12 3 of 10
## 44: ABCA13 Responders Rest 3 of 10 3 of 12
## 45: DSCAM Non-responders Rest 3 of 12 3 of 10
## 46: DSCAM Responders Rest 3 of 10 3 of 12
## 47: MYO15A Non-responders Rest 3 of 12 3 of 10
## 48: MYO15A Responders Rest 3 of 10 3 of 12
## 49: DNAH6 Non-responders Rest 3 of 12 3 of 10
## 50: DNAH6 Responders Rest 3 of 10 3 of 12
## 51: CNTN5 Non-responders Rest 3 of 12 3 of 10
## 52: CNTN5 Responders Rest 3 of 10 3 of 12
## 53: BRAF Non-responders Rest 8 of 12 6 of 10
## 54: BRAF Responders Rest 6 of 10 8 of 12
## 55: PCLO Non-responders Rest 5 of 12 4 of 10
## 56: PCLO Responders Rest 4 of 10 5 of 12
## Hugo_Symbol Group1 Group2 n_mutated_group1 n_mutated_group2
## p_value OR OR_low OR_high fdr
## <num> <num> <num> <num> <num>
## 1: 0.04273126 0.1393973 0.009829588 1.164437 0.3900929
## 2: 0.04273126 7.1737384 0.858784223 101.733669 0.3900929
## 3: 0.05572755 0.1024623 0.001773299 1.250950 0.3900929
## 4: 0.05572755 9.7596867 0.799392236 563.920542 0.3900929
## 5: 0.05572755 0.1024623 0.001773299 1.250950 0.3900929
## 6: 0.05572755 9.7596867 0.799392236 563.920542 0.3900929
## 7: 0.05572755 0.1024623 0.001773299 1.250950 0.3900929
## 8: 0.05572755 9.7596867 0.799392236 563.920542 0.3900929
## 9: 0.09907121 0.1943170 0.014105470 1.601714 0.5345717
## 10: 0.09907121 5.1462289 0.624331001 70.894480 0.5345717
## 11: 0.17182663 0.2166050 0.015292506 1.914989 0.5345717
## 12: 0.17182663 4.6166977 0.522196276 65.391505 0.5345717
## 13: 0.17182663 0.2166050 0.015292506 1.914989 0.5345717
## 14: 0.17182663 4.6166977 0.522196276 65.391505 0.5345717
## 15: 0.17182663 0.2166050 0.015292506 1.914989 0.5345717
## 16: 0.17182663 4.6166977 0.522196276 65.391505 0.5345717
## 17: 0.17182663 0.2166050 0.015292506 1.914989 0.5345717
## 18: 0.17182663 4.6166977 0.522196276 65.391505 0.5345717
## 19: 0.34763379 0.3176677 0.022167729 3.025373 0.6952676
## 20: 0.34763379 3.1479440 0.330537773 45.110621 0.6952676
## 21: 0.34763379 0.3176677 0.022167729 3.025373 0.6952676
## 22: 0.34763379 3.1479440 0.330537773 45.110621 0.6952676
## 23: 0.34763379 0.3176677 0.022167729 3.025373 0.6952676
## 24: 0.34763379 3.1479440 0.330537773 45.110621 0.6952676
## 25: 0.34763379 0.3176677 0.022167729 3.025373 0.6952676
## 26: 0.34763379 3.1479440 0.330537773 45.110621 0.6952676
## 27: 0.34763379 0.3176677 0.022167729 3.025373 0.6952676
## 28: 0.34763379 3.1479440 0.330537773 45.110621 0.6952676
## 29: 0.64617426 1.9386158 0.204565986 27.339825 0.9809353
## 30: 0.64617426 0.5158320 0.036576679 4.888398 0.9809353
## 31: 0.65170279 0.5163825 0.054045583 4.332564 0.9809353
## 32: 0.65170279 1.9365490 0.230810220 18.502900 0.9809353
## 33: 0.65170279 0.5163825 0.054045583 4.332564 0.9809353
## 34: 0.65170279 1.9365490 0.230810220 18.502900 0.9809353
## 35: 0.65170279 0.5163825 0.054045583 4.332564 0.9809353
## 36: 0.65170279 1.9365490 0.230810220 18.502900 0.9809353
## 37: 0.66563467 0.5163899 0.064087475 3.791432 0.9809353
## 38: 0.66563467 1.9365213 0.263752588 15.603673 0.9809353
## 39: 1.00000000 1.0000000 0.138668627 7.211437 1.0000000
## 40: 1.00000000 1.0000000 0.138668627 7.211437 1.0000000
## 41: 1.00000000 1.1585362 0.137618146 10.854999 1.0000000
## 42: 1.00000000 0.8631582 0.092123452 7.266484 1.0000000
## 43: 1.00000000 0.7867773 0.078561110 7.821058 1.0000000
## 44: 1.00000000 1.2710078 0.127859942 12.728944 1.0000000
## 45: 1.00000000 0.7867773 0.078561110 7.821058 1.0000000
## 46: 1.00000000 1.2710078 0.127859942 12.728944 1.0000000
## 47: 1.00000000 0.7867773 0.078561110 7.821058 1.0000000
## 48: 1.00000000 1.2710078 0.127859942 12.728944 1.0000000
## 49: 1.00000000 0.7867773 0.078561110 7.821058 1.0000000
## 50: 1.00000000 1.2710078 0.127859942 12.728944 1.0000000
## 51: 1.00000000 0.7867773 0.078561110 7.821058 1.0000000
## 52: 1.00000000 1.2710078 0.127859942 12.728944 1.0000000
## 53: 1.00000000 1.3158848 0.166513699 10.588251 1.0000000
## 54: 1.00000000 0.7599449 0.094444307 6.005512 1.0000000
## 55: 1.00000000 1.0680540 0.144162138 8.236116 1.0000000
## 56: 1.00000000 0.9362822 0.121416450 6.936634 1.0000000
## p_value OR OR_low OR_high fdr
##
## $cf_sizes
## cf N
## <char> <int>
## 1: Non-responders 12
## 2: Responders 10
##
## $clinicalFeature
## [1] "Patient_Response"## Writes maf summary to an output file with basename mm909
write.mafSummary(maf = mm909, basename = file.path(results_fip_path, 'mm909'))11.1.3 Count SNP, DNP, Missense, Nonsense, Splice, Nonstop
maf_df_annotated %>%
dplyr::filter(Variant_Type == "SNP") %>%
dplyr::count() %>%
dplyr::rename(SNP = n)## # A tibble: 1 × 1
## SNP
## <int>
## 1 10489maf_df_annotated %>%
dplyr::filter(Variant_Type == "DNP") %>%
dplyr::count() %>%
dplyr::rename(DNP = n)## # A tibble: 1 × 1
## DNP
## <int>
## 1 304maf_df_annotated %>%
dplyr::filter(Variant_Classification == "Missense_Mutation") %>%
dplyr::count() %>%
dplyr::rename(Missense_Mutation = n)## # A tibble: 1 × 1
## Missense_Mutation
## <int>
## 1 6345maf_df_annotated %>%
dplyr::filter(Variant_Classification == "Nonsense_Mutation") %>%
dplyr::count() %>%
dplyr::rename(Nonsense_Mutation = n)## # A tibble: 1 × 1
## Nonsense_Mutation
## <int>
## 1 381maf_df_annotated %>%
dplyr::filter(Variant_Classification == "Splice_Site") %>%
dplyr::count() %>%
dplyr::rename(Splice_Site = n)## # A tibble: 1 × 1
## Splice_Site
## <int>
## 1 285maf_df_annotated %>%
dplyr::filter(Variant_Classification == "Nonstop_Mutation") %>%
dplyr::count() %>%
dplyr::rename(Nonstop_Mutation = n)## # A tibble: 1 × 1
## Nonstop_Mutation
## <int>
## 1 411.1.4 Visualization
# Fig3
# Define output path
plot_path1 <- file.path(figures_fip_path, "maf_summary.png")
# Open a PNG device
png(filename = plot_path1, width = 3000, height = 1600, res = 300)
# Generate the plot
plotmafSummary(maf = mm909, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE, titvRaw = FALSE)
# Close the device
dev.off()## quartz_off_screen
## 2knitr::include_graphics(plot_path1)
# Get the summary of mutations per sample
sample_summary <- getSampleSummary(mm909)
# View the sample summary to understand the data structure
print(sample_summary)## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Nonstop_Mutation
## <fctr> <int> <int> <int>
## 1: MM909_22_1 1271 65 1
## 2: MM909_03_1 1159 67 0
## 3: MM909_26_1 828 47 0
## 4: MM909_42_1 398 24 0
## 5: MM909_43_1 349 27 0
## 6: MM909_15_2 342 21 0
## 7: MM909_46_1 287 22 0
## 8: MM909_16_1 285 19 0
## 9: MM909_17_1 237 8 1
## 10: MM909_25_1 168 12 0
## 11: MM909_11_1 155 14 0
## 12: MM909_31_1 150 12 0
## 13: MM909_40_1 138 9 1
## 14: MM909_02_1 111 5 0
## 15: MM909_35_1 101 3 0
## 16: MM909_34_2 87 9 0
## 17: MM909_37_1 76 5 0
## 18: MM909_27_1 68 2 1
## 19: MM909_06_1 46 4 0
## 20: MM909_36_1 45 4 0
## 21: MM909_14_1 29 2 0
## 22: MM909_47_1 15 0 0
## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Nonstop_Mutation
## Splice_Site total
## <int> <num>
## 1: 52 1389
## 2: 59 1285
## 3: 28 903
## 4: 14 436
## 5: 16 392
## 6: 13 376
## 7: 10 319
## 8: 11 315
## 9: 19 265
## 10: 6 186
## 11: 9 178
## 12: 11 173
## 13: 8 156
## 14: 9 125
## 15: 3 107
## 16: 4 100
## 17: 2 83
## 18: 2 73
## 19: 2 52
## 20: 1 50
## 21: 4 35
## 22: 2 17
## Splice_Site total# Oncoplot for top ten mutated genes.
# Define output path
plot_path2 <- file.path(figures_fip_path, "maf_top50.png")
# Open a PNG device
png(filename = plot_path2, width = 3500, height = 4000, res = 300)
#Color coding for response
resp_colors = c("#009E73", "#D55E00")
names(resp_colors) = c("Responders", "Non-responders")
response_colors = list(Patient_Response = resp_colors)
#Color coding for sample id
sample_colors <- c(
"#56B4E9", "#999999", "#8A2BE2", "#E69F00", "#D55E00", "#CC79A7",
"#009E73", "#F0E442", "#0072B2", "#FF69B4", "#FFD700", "#000000",
"#00CED1", "#4B0082", "#1E90FF", "#FF6347", "#32CD32", "#8B4513",
"#FF4500", "#B22222", "#FFDAB9", "#20B2AA"
)
names(sample_colors) = unique(maf_df_annotated$Sample_ID)
sample_id_colors = list(Sample_ID = sample_colors)
print(response_colors)## $Patient_Response
## Responders Non-responders
## "#009E73" "#D55E00"# Generate plot
oncoplot(maf = mm909, top = 50, drawColBar = FALSE, clinicalFeatures = c('Patient_Response'), sortByAnnotation = TRUE, annotationColor = response_colors, anno_height = 0.25, legend_height = 1)
# Close the device
dev.off()## quartz_off_screen
## 2knitr::include_graphics(plot_path2)
mm909.titv = titv(maf = mm909, plot = FALSE, useSyn = TRUE)
# Plot titv summary
plotTiTv(res = mm909.titv)
mm909.mutload = tcgaCompare(maf = mm909, cohortName = 'Example-mm909', logscale = TRUE, capture_size = 50)
11.2 LoF mutations
11.2.1 Load data
# Filter for LoF mutations
lof_vep_maf <- lof_vep_df_reduced %>%
separate(Location, into = c("Chromosome", "Position"), sep = ":", convert = TRUE) %>%
separate(Position, into = c("Start_position", "End_position"), sep = "-", convert = TRUE) %>%
dplyr::rename(alt_allele = Allele)
# Rename
maf_df_annotated2 <- maf_df_annotated %>%
dplyr::rename(sample_id = Sample_ID)
# Filter maf_df_annotated based on matching rows in lof_vep_df_reduced
lof_maf_df <- maf_df_annotated2 %>%
semi_join(lof_vep_maf, by = c("sample_id", "ensembl_id", "entrez_id", "Chromosome", "Start_position", "End_position", "alt_allele"))
# Load maf with clinical data
mm909 = read.maf(maf = lof_maf_df, clinicalData = clinical_resp_df)## -Validating
## -Silent variants: 7
## -Summarizing
## --Possible FLAGS among top ten genes:
## MUC16
## TTN
## -Processing clinical data
## -Finished in 0.773s elapsed (0.596s cpu)11.2.2 Summaries
## Shows sample summry.
sample_sum <- getSampleSummary(mm909)
getSampleSummary(mm909)## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Splice_Site total
## <fctr> <int> <int> <int> <num>
## 1: MM909_22_1 840 1 16 857
## 2: MM909_03_1 769 0 30 799
## 3: MM909_26_1 564 0 7 571
## 4: MM909_42_1 268 0 4 272
## 5: MM909_15_2 243 1 4 248
## 6: MM909_43_1 240 0 3 243
## 7: MM909_16_1 190 0 2 192
## 8: MM909_46_1 178 0 5 183
## 9: MM909_17_1 162 0 7 169
## 10: MM909_25_1 120 0 1 121
## 11: MM909_11_1 104 0 6 110
## 12: MM909_31_1 97 0 4 101
## 13: MM909_40_1 88 0 2 90
## 14: MM909_02_1 73 0 5 78
## 15: MM909_35_1 72 0 2 74
## 16: MM909_34_2 62 0 1 63
## 17: MM909_37_1 52 0 1 53
## 18: MM909_27_1 41 0 1 42
## 19: MM909_36_1 33 0 1 34
## 20: MM909_06_1 30 0 0 30
## 21: MM909_14_1 13 0 1 14
## 22: MM909_47_1 9 0 1 10
## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Splice_Site total## Shows gene summary.
getGeneSummary(mm909)## Hugo_Symbol Missense_Mutation Nonsense_Mutation Splice_Site total
## <char> <int> <int> <int> <num>
## 1: BRAF 15 0 0 15
## 2: MIR548N 17 0 0 17
## 3: MUC16 23 0 0 23
## 4: TTN 18 0 0 18
## 5: PCLO 10 0 0 10
## ---
## 3196: ZPLD1 1 0 0 1
## 3197: ZSCAN18 1 0 0 1
## 3198: ZSCAN31 1 0 0 1
## 3199: ZSCAN5A 1 0 0 1
## 3200: ZWILCH 1 0 0 1
## MutatedSamples AlteredSamples
## <int> <int>
## 1: 13 13
## 2: 11 11
## 3: 10 10
## 4: 8 8
## 5: 7 7
## ---
## 3196: 1 1
## 3197: 1 1
## 3198: 1 1
## 3199: 1 1
## 3200: 1 1## Shows clinical data associated with samples
getClinicalData(mm909)## Sample_ID Sample.Timepoint AJCC.Stage PFS.Time PFS.Event
## <char> <char> <char> <char> <char>
## 1: MM909_02 Before TILs M1b 2.00 1
## 2: MM909_03 Before TILs M1c 3.80 1
## 3: MM909_06 Before TILs M1c 2.30 1
## 4: MM909_11 Before TILs M1a 13.10 1
## 5: MM909_14 Before TILs M1c 2.50 1
## 6: MM909_15 Before TILs M1b 67.13 0
## 7: MM909_16 Before TILs M1c 3.90 1
## 8: MM909_17 Before TILs M1c 64.87 0
## 9: MM909_22 After Ipilimumab/Before TILs M1c 32.10 0
## 10: MM909_25 Before TILs M1b 3.77 1
## 11: MM909_26 Before TILs M1c 52.70 0
## 12: MM909_27 Before Vemurafenib/Before TILs M1c 2.00 1
## 13: MM909_31 Before TILs M1c 11.30 1
## 14: MM909_34 Before TILs M1c 2.83 1
## 15: MM909_35 During Vemurafenib/Before TILs M1c 3.13 1
## 16: MM909_36 During Vemurafenib/Before TILs M1c 8.23 1
## 17: MM909_37 Before TILs M1c 3.03 1
## 18: MM909_40 Before TILs M1c 3.73 1
## 19: MM909_42 Before TILs M1c 42.30 0
## 20: MM909_43 Before TILs M1c 1.93 1
## 21: MM909_46 Before TILs M1c 5.83 1
## 22: MM909_47 Before TILs M1c 3.27 1
## Sample_ID Sample.Timepoint AJCC.Stage PFS.Time PFS.Event
## OS.Time OS.Event Type.of.Lesion Type.of.Primary RECIST Patient_Response
## <char> <char> <char> <char> <char> <char>
## 1: 6.9 1 LN skin PD Non-responders
## 2: 11.4 1 SC unknown SD Non-responders
## 3: 4.6 1 LN skin PD Non-responders
## 4: 71.5 0 LN skin CR Responders
## 5: 3.2 1 LN unknown PD Non-responders
## 6: 67.1 0 LN skin CR Responders
## 7: 65.3 0 SC skin SD Responders
## 8: 64.9 0 LN skin PR Responders
## 9: 32.1 1 <NA> skin PR Responders
## 10: 5.5 1 LN skin SD Non-responders
## 11: 52.7 0 IM skin CR Responders
## 12: 3.5 1 SC skin PD Non-responders
## 13: 20.1 1 SC mucosa PR Responders
## 14: 5.0 1 LN mucosa SD Non-responders
## 15: 23.7 1 SC skin SD Non-responders
## 16: 24.6 1 IA skin PR Responders
## 17: 12.1 1 SC skin SD Non-responders
## 18: 14.0 1 IA skin SD Non-responders
## 19: 42.3 0 SC skin CR Responders
## 20: 22.8 1 SC unknown PD Non-responders
## 21: 24.8 1 LN skin SD Responders
## 22: 18.9 1 pleura unknown SD Non-responders
## OS.Time OS.Event Type.of.Lesion Type.of.Primary RECIST Patient_Response
## mut_load Tumor_Sample_Barcode
## <char> <char>
## 1: 199 MM909_02_1
## 2: 2036 MM909_03_1
## 3: 86 MM909_06_1
## 4: 277 MM909_11_1
## 5: 62 MM909_14_1
## 6: 581 MM909_15_2
## 7: 493 MM909_16_1
## 8: 366 MM909_17_1
## 9: 2200 MM909_22_1
## 10: 295 MM909_25_1
## 11: 1378 MM909_26_1
## 12: 110 MM909_27_1
## 13: 246 MM909_31_1
## 14: 157 MM909_34_2
## 15: 180 MM909_35_1
## 16: 85 MM909_36_1
## 17: 131 MM909_37_1
## 18: 214 MM909_40_1
## 19: 654 MM909_42_1
## 20: 601 MM909_43_1
## 21: 488 MM909_46_1
## 22: 23 MM909_47_1
## mut_load Tumor_Sample_Barcode## Shows all fields in MAF
getFields(mm909)## [1] "sample_id" "ensembl_id" "entrez_id"
## [4] "Hugo_Symbol" "contig" "position"
## [7] "context" "ref_allele" "alt_allele"
## [10] "tumor_name" "normal_name" "score"
## [13] "dbsnp_site" "covered" "power"
## [16] "tumor_power" "normal_power" "total_pairs"
## [19] "improper_pairs" "map_Q0_reads" "t_lod_fstar"
## [22] "tumor_f" "contaminant_fraction" "contaminant_lod"
## [25] "t_ref_count" "t_alt_count" "t_ref_sum"
## [28] "t_alt_sum" "t_ref_max_mapq" "t_alt_max_mapq"
## [31] "t_ins_count" "t_del_count" "normal_best_gt"
## [34] "init_n_lod" "n_ref_count" "n_alt_count"
## [37] "n_ref_sum" "n_alt_sum" "judgement"
## [40] "chrom23" "Chromosome" "Start_Position"
## [43] "End_Position" "Strand" "Tumor_Sample_Barcode"
## [46] "Center" "NCBI_Build" "Variant_Classification"
## [49] "Variant_Type" "Reference_Allele" "Tumor_Seq_Allele1"
## [52] "Tumor_Seq_Allele2" "cDNA_Change" "Codon_Change"
## [55] "Protein_Change"## Shows clinical enrichment
clinicalEnrichment(mm909, "Patient_Response")##
## Non-responders Responders
## 12 10
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## <char> <char> <char> <char> <char>
## 1: BRAF Responders Non-responders 5 of 10 8 of 12
## 2: BRAF <NA> <NA> <NA> <NA>
## 3: BRAF <NA> <NA> <NA> <NA>
## 4: MIR548N Responders Non-responders 7 of 10 4 of 12
## 5: MIR548N <NA> <NA> <NA> <NA>
## 6: MIR548N <NA> <NA> <NA> <NA>
## 7: MUC16 Responders Non-responders 8 of 10 2 of 12
## 8: MUC16 <NA> <NA> <NA> <NA>
## 9: MUC16 <NA> <NA> <NA> <NA>
## 10: TTN Responders Non-responders 5 of 10 3 of 12
## 11: TTN <NA> <NA> <NA> <NA>
## 12: TTN <NA> <NA> <NA> <NA>
## 13: PCLO Responders Non-responders 3 of 10 4 of 12
## 14: PCLO <NA> <NA> <NA> <NA>
## 15: PCLO <NA> <NA> <NA> <NA>
## 16: ABCA13 Responders Non-responders 3 of 10 3 of 12
## 17: ABCA13 <NA> <NA> <NA> <NA>
## 18: ABCA13 <NA> <NA> <NA> <NA>
## 19: CSMD1 Responders Non-responders 3 of 10 3 of 12
## 20: CSMD1 <NA> <NA> <NA> <NA>
## 21: CSMD1 <NA> <NA> <NA> <NA>
## 22: MYH8 Responders Non-responders 5 of 10 1 of 12
## 23: MYH8 <NA> <NA> <NA> <NA>
## 24: MYH8 <NA> <NA> <NA> <NA>
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## fdr Analysis Group1 Group2 n_mutated_group1
## <num> <char> <char> <char> <char>
## 1: 0.665634675 Pairwise <NA> <NA> <NA>
## 2: NA Group Non-responders Rest 8 of 12
## 3: NA Group Responders Rest 5 of 10
## 4: 0.198380567 Pairwise <NA> <NA> <NA>
## 5: NA Group Non-responders Rest 4 of 12
## 6: NA Group Responders Rest 7 of 10
## 7: 0.008284285 Pairwise <NA> <NA> <NA>
## 8: NA Group Non-responders Rest 2 of 12
## 9: NA Group Responders Rest 8 of 10
## 10: 0.377708978 Pairwise <NA> <NA> <NA>
## 11: NA Group Non-responders Rest 3 of 12
## 12: NA Group Responders Rest 5 of 10
## 13: 1.000000000 Pairwise <NA> <NA> <NA>
## 14: NA Group Non-responders Rest 4 of 12
## 15: NA Group Responders Rest 3 of 10
## 16: 1.000000000 Pairwise <NA> <NA> <NA>
## 17: NA Group Non-responders Rest 3 of 12
## 18: NA Group Responders Rest 3 of 10
## 19: 1.000000000 Pairwise <NA> <NA> <NA>
## 20: NA Group Non-responders Rest 3 of 12
## 21: NA Group Responders Rest 3 of 10
## 22: 0.055727554 Pairwise <NA> <NA> <NA>
## 23: NA Group Non-responders Rest 1 of 12
## 24: NA Group Responders Rest 5 of 10
## fdr Analysis Group1 Group2 n_mutated_group1
## n_mutated_group2 p_value OR OR_low OR_high
## <char> <num> <num> <num> <num>
## 1: <NA> NA NA NA NA
## 2: 5 of 10 0.665634675 1.93652133 0.263752588 15.6036729
## 3: 8 of 12 0.665634675 0.51638987 0.064087475 3.7914320
## 4: <NA> NA NA NA NA
## 5: 7 of 10 0.198380567 0.23140461 0.023782661 1.7297317
## 6: 4 of 12 0.198380567 4.32143517 0.578124339 42.0474391
## 7: <NA> NA NA NA NA
## 8: 8 of 10 0.008284285 0.06077883 0.003443304 0.5897034
## 9: 2 of 12 0.008284285 16.45309815 1.695767733 290.4187413
## 10: <NA> NA NA NA NA
## 11: 5 of 10 0.377708978 0.35137037 0.036968351 2.7360577
## 12: 3 of 12 0.377708978 2.84599982 0.365489374 27.0501651
## 13: <NA> NA NA NA NA
## 14: 3 of 10 1.000000000 1.15853624 0.137618146 10.8549992
## 15: 4 of 12 1.000000000 0.86315816 0.092123452 7.2664836
## 16: <NA> NA NA NA NA
## 17: 3 of 10 1.000000000 0.78677725 0.078561110 7.8210578
## 18: 3 of 12 1.000000000 1.27100777 0.127859942 12.7289443
## 19: <NA> NA NA NA NA
## 20: 3 of 10 1.000000000 0.78677725 0.078561110 7.8210578
## 21: 3 of 12 1.000000000 1.27100777 0.127859942 12.7289443
## 22: <NA> NA NA NA NA
## 23: 5 of 10 0.055727554 0.10246231 0.001773299 1.2509504
## 24: 1 of 12 0.055727554 9.75968674 0.799392236 563.9205419
## n_mutated_group2 p_value OR OR_low OR_high## $pairwise_comparision
## Hugo_Symbol Feature_1 Feature_2 n_mutated_Feature1 n_mutated_Feature2
## <char> <char> <char> <char> <char>
## 1: MUC16 Responders Non-responders 8 of 10 2 of 12
## 2: MYH8 Responders Non-responders 5 of 10 1 of 12
## 3: MIR548N Responders Non-responders 7 of 10 4 of 12
## 4: TTN Responders Non-responders 5 of 10 3 of 12
## 5: BRAF Responders Non-responders 5 of 10 8 of 12
## 6: PCLO Responders Non-responders 3 of 10 4 of 12
## 7: ABCA13 Responders Non-responders 3 of 10 3 of 12
## 8: CSMD1 Responders Non-responders 3 of 10 3 of 12
## fdr
## <num>
## 1: 0.008284285
## 2: 0.055727554
## 3: 0.198380567
## 4: 0.377708978
## 5: 0.665634675
## 6: 1.000000000
## 7: 1.000000000
## 8: 1.000000000
##
## $groupwise_comparision
## Hugo_Symbol Group1 Group2 n_mutated_group1 n_mutated_group2
## <char> <char> <char> <char> <char>
## 1: MUC16 Non-responders Rest 2 of 12 8 of 10
## 2: MUC16 Responders Rest 8 of 10 2 of 12
## 3: MYH8 Non-responders Rest 1 of 12 5 of 10
## 4: MYH8 Responders Rest 5 of 10 1 of 12
## 5: MIR548N Non-responders Rest 4 of 12 7 of 10
## 6: MIR548N Responders Rest 7 of 10 4 of 12
## 7: TTN Non-responders Rest 3 of 12 5 of 10
## 8: TTN Responders Rest 5 of 10 3 of 12
## 9: BRAF Non-responders Rest 8 of 12 5 of 10
## 10: BRAF Responders Rest 5 of 10 8 of 12
## 11: PCLO Non-responders Rest 4 of 12 3 of 10
## 12: PCLO Responders Rest 3 of 10 4 of 12
## 13: ABCA13 Non-responders Rest 3 of 12 3 of 10
## 14: ABCA13 Responders Rest 3 of 10 3 of 12
## 15: CSMD1 Non-responders Rest 3 of 12 3 of 10
## 16: CSMD1 Responders Rest 3 of 10 3 of 12
## p_value OR OR_low OR_high fdr
## <num> <num> <num> <num> <num>
## 1: 0.008284285 0.06077883 0.003443304 0.5897034 0.06627428
## 2: 0.008284285 16.45309815 1.695767733 290.4187413 0.06627428
## 3: 0.055727554 0.10246231 0.001773299 1.2509504 0.22291022
## 4: 0.055727554 9.75968674 0.799392236 563.9205419 0.22291022
## 5: 0.198380567 0.23140461 0.023782661 1.7297317 0.52901484
## 6: 0.198380567 4.32143517 0.578124339 42.0474391 0.52901484
## 7: 0.377708978 0.35137037 0.036968351 2.7360577 0.75541796
## 8: 0.377708978 2.84599982 0.365489374 27.0501651 0.75541796
## 9: 0.665634675 1.93652133 0.263752588 15.6036729 1.00000000
## 10: 0.665634675 0.51638987 0.064087475 3.7914320 1.00000000
## 11: 1.000000000 1.15853624 0.137618146 10.8549992 1.00000000
## 12: 1.000000000 0.86315816 0.092123452 7.2664836 1.00000000
## 13: 1.000000000 0.78677725 0.078561110 7.8210578 1.00000000
## 14: 1.000000000 1.27100777 0.127859942 12.7289443 1.00000000
## 15: 1.000000000 0.78677725 0.078561110 7.8210578 1.00000000
## 16: 1.000000000 1.27100777 0.127859942 12.7289443 1.00000000
##
## $cf_sizes
## cf N
## <char> <int>
## 1: Non-responders 12
## 2: Responders 10
##
## $clinicalFeature
## [1] "Patient_Response"## Writes maf summary to an output file with basename mm909
write.mafSummary(maf = mm909, basename = file.path(results_fip_path, 'mm909_lof'))11.2.3 Visualization
# Define output path
plot_path1 <- file.path(figures_fip_path, "maf_lof_summary.png")
# Open a PNG device
png(filename = plot_path1, width = 3000, height = 1600, res = 300)
# Generate the plot
plotmafSummary(maf = mm909, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE, titvRaw = FALSE)
# Close the device
dev.off()## quartz_off_screen
## 2knitr::include_graphics(plot_path1)
# Get the summary of mutations per sample
sample_summary <- getSampleSummary(mm909)
# View the sample summary to understand the data structure
print(sample_summary)## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Splice_Site total
## <fctr> <int> <int> <int> <num>
## 1: MM909_22_1 840 1 16 857
## 2: MM909_03_1 769 0 30 799
## 3: MM909_26_1 564 0 7 571
## 4: MM909_42_1 268 0 4 272
## 5: MM909_15_2 243 1 4 248
## 6: MM909_43_1 240 0 3 243
## 7: MM909_16_1 190 0 2 192
## 8: MM909_46_1 178 0 5 183
## 9: MM909_17_1 162 0 7 169
## 10: MM909_25_1 120 0 1 121
## 11: MM909_11_1 104 0 6 110
## 12: MM909_31_1 97 0 4 101
## 13: MM909_40_1 88 0 2 90
## 14: MM909_02_1 73 0 5 78
## 15: MM909_35_1 72 0 2 74
## 16: MM909_34_2 62 0 1 63
## 17: MM909_37_1 52 0 1 53
## 18: MM909_27_1 41 0 1 42
## 19: MM909_36_1 33 0 1 34
## 20: MM909_06_1 30 0 0 30
## 21: MM909_14_1 13 0 1 14
## 22: MM909_47_1 9 0 1 10
## Tumor_Sample_Barcode Missense_Mutation Nonsense_Mutation Splice_Site total# Fig6
# Oncoplot for top fifty mutated genes.
# Define output path
plot_path2 <- file.path(figures_fip_path, "maf_lof_top50.png")
# Open a PNG device
png(filename = plot_path2, width = 3500, height = 4000, res = 300)
#Color coding for response
resp_colors = c("#009E73", "#D55E00")
names(resp_colors) = c("Responders", "Non-responders")
response_colors = list(Patient_Response = resp_colors)
#Color coding for sample id
sample_colors <- c(
"#56B4E9", "#999999", "#8A2BE2", "#E69F00", "#D55E00", "#CC79A7",
"#009E73", "#F0E442", "#0072B2", "#FF69B4", "#FFD700", "#000000",
"#00CED1", "#4B0082", "#1E90FF", "#FF6347", "#32CD32", "#8B4513",
"#FF4500", "#B22222", "#FFDAB9", "#20B2AA"
)
names(sample_colors) = unique(maf_df_annotated$Sample_ID)
sample_id_colors = list(Sample_ID = sample_colors)
print(response_colors)## $Patient_Response
## Responders Non-responders
## "#009E73" "#D55E00"# Generate plot
oncoplot(maf = mm909, top = 50, drawColBar = FALSE, clinicalFeatures = 'Patient_Response', sortByAnnotation = TRUE, annotationColor = response_colors, anno_height = 0.25, legend_height = 1, showTitle = FALSE, drawBox = TRUE, legendFontSize = 1.7, annotationFontSize = 1.7)
# Close the device
dev.off()## quartz_off_screen
## 2knitr::include_graphics(plot_path2)
mm909.titv = titv(maf = mm909, plot = FALSE, useSyn = TRUE)
# Plot titv summary
plotTiTv(res = mm909.titv)
mm909.mutload = tcgaCompare(maf = mm909, cohortName = 'Example-mm909', logscale = TRUE, capture_size = 50)
12 Explore shared LoF mutated genes
# Fig4
resp_df <- clinical_resp_df %>%
dplyr::select(Sample_ID, Patient_Response) %>%
dplyr::rename(sample_id = Sample_ID,
patient_response = Patient_Response)
maf_df_resp <- lof_maf_df %>%
dplyr::left_join(resp_df, by="sample_id")
# Get genes for each response group
responder_genes <- maf_df_resp %>%
dplyr::filter(patient_response == "Responders") %>%
pull(hugo_symbol) %>%
unique()
non_responder_genes <- maf_df_resp %>%
dplyr::filter(patient_response == "Non-responders") %>%
pull(hugo_symbol) %>%
unique()
# Identify unique and shared genes
shared_genes <- intersect(responder_genes, non_responder_genes)
unique_responder_genes <- setdiff(responder_genes, shared_genes)
unique_non_responder_genes <- setdiff(non_responder_genes, shared_genes)
# Define the lists for the Venn diagram
gene_lists <- list(
Responder = responder_genes,
Non_responder = non_responder_genes
)
# Set up PNG device to save the plot
venn_path <- file.path(figures_fip_path, "venn_diagram_shared_genes_lof.png")
png(filename = venn_path, width = 1000, height = 700, res = 300)
# Generate Venn diagram
venn.plot <- draw.pairwise.venn(
area1 = length(responder_genes),
area2 = length(non_responder_genes),
cross.area = length(shared_genes),
category = c("Responders", "Non-responders"),
fill = c("#009E73", "#D55E00"),
cex = 1, # Adjust font size of numbers
cat.cex = 1, # Adjust category label font size
cat.col = c("black", "black"),
cat.pos = 0, # Positions for category names (0 degrees, on top)
cat.dist = 0.07, # Increase this if labels overlap with the circles
euler.d = TRUE, # Force circles to be more circular
lwd = 2, # Line width
filename = NULL
)
# Display the plot
grid.newpage()
grid.draw(venn.plot)
# Save the plot
dev.off()## quartz_off_screen
## 2knitr::include_graphics(venn_path)
13 Session Info
sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.7.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Copenhagen
## tzcode source: internal
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ComplexHeatmap_2.18.0 GO.db_3.18.0
## [3] mygene_1.38.0 ggupset_0.3.0
## [5] clusterProfiler_4.10.1 limma_3.58.1
## [7] fgsea_1.28.0 EnsDb.Hsapiens.v86_2.99.0
## [9] EnsDb.Hsapiens.v75_2.99.0 ensembldb_2.26.0
## [11] AnnotationFilter_1.26.0 GenomicFeatures_1.54.4
## [13] GSEABase_1.64.0 graph_1.80.0
## [15] annotate_1.80.0 XML_3.99-0.16.1
## [17] AnnotationDbi_1.64.1 maftools_2.18.0
## [19] biomaRt_2.58.2 VariantAnnotation_1.48.1
## [21] Rsamtools_2.18.0 Biostrings_2.70.3
## [23] XVector_0.42.0 SummarizedExperiment_1.32.0
## [25] Biobase_2.62.0 GenomicRanges_1.54.1
## [27] GenomeInfoDb_1.38.8 IRanges_2.36.0
## [29] S4Vectors_0.40.2 MatrixGenerics_1.14.0
## [31] matrixStats_1.3.0 BiocGenerics_0.48.1
## [33] magrittr_2.0.3 dendextend_1.17.1
## [35] corrplot_0.92 broom_1.0.5
## [37] ggrepel_0.9.5 ggplotify_0.1.2
## [39] pheatmap_1.0.12 cowplot_1.1.3
## [41] ggnewscale_0.4.10 reshape2_1.4.4
## [43] ggdendro_0.2.0 ggpubr_0.6.0
## [45] patchwork_1.2.0 gridtext_0.1.5
## [47] circlize_0.4.16 VennDiagram_1.7.3
## [49] futile.logger_1.4.3 HGNChelper_0.8.1
## [51] msigdbr_7.5.1 jsonlite_1.8.8
## [53] httr_1.4.7 DT_0.33
## [55] knitr_1.45 lubridate_1.9.3
## [57] forcats_1.0.0 stringr_1.5.1
## [59] dplyr_1.1.4 purrr_1.0.2
## [61] readr_2.1.5 tidyr_1.3.1
## [63] tibble_3.2.1 ggplot2_3.5.1
## [65] tidyverse_2.0.0 readxl_1.4.3
## [67] rprojroot_2.0.4
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.4 ProtGenerics_1.34.0 bitops_1.0-7
## [4] enrichplot_1.22.0 doParallel_1.0.17 HDO.db_0.99.1
## [7] RColorBrewer_1.1-3 tools_4.3.2 backports_1.4.1
## [10] utf8_1.2.4 R6_2.5.1 lazyeval_0.2.2
## [13] GetoptLong_1.0.5 withr_3.0.0 prettyunits_1.2.0
## [16] gridExtra_2.3 textshaping_0.3.7 cli_3.6.2
## [19] formatR_1.14 scatterpie_0.2.2 labeling_0.4.3
## [22] sass_0.4.9 systemfonts_1.1.0 yulab.utils_0.1.4
## [25] gson_0.1.0 foreign_0.8-86 R.utils_2.12.3
## [28] DOSE_3.28.2 BSgenome_1.70.2 rstudioapi_0.16.0
## [31] RSQLite_2.3.6 generics_0.1.3 gridGraphics_0.5-1
## [34] shape_1.4.6.1 BiocIO_1.12.0 crosstalk_1.2.1
## [37] car_3.1-2 Matrix_1.6-5 fansi_1.0.6
## [40] abind_1.4-5 R.methodsS3_1.8.2 lifecycle_1.0.4
## [43] yaml_2.3.8 carData_3.0-5 qvalue_2.34.0
## [46] SparseArray_1.2.4 BiocFileCache_2.10.2 blob_1.2.4
## [49] crayon_1.5.2 lattice_0.22-6 KEGGREST_1.42.0
## [52] pillar_1.9.0 rjson_0.2.21 codetools_0.2-20
## [55] fastmatch_1.1-4 glue_1.7.0 ggfun_0.1.4
## [58] data.table_1.15.4 vctrs_0.6.5 png_0.1-8
## [61] treeio_1.26.0 cellranger_1.1.0 gtable_0.3.5
## [64] gsubfn_0.7 cachem_1.0.8 xfun_0.44
## [67] S4Arrays_1.2.1 tidygraph_1.3.1 survival_3.6-4
## [70] iterators_1.0.14 statmod_1.5.0 nlme_3.1-164
## [73] ggtree_3.10.1 bit64_4.0.5 progress_1.2.3
## [76] filelock_1.0.3 bslib_0.7.0 rpart_4.1.23
## [79] colorspace_2.1-0 DBI_1.2.2 Hmisc_5.1-2
## [82] nnet_7.3-19 DNAcopy_1.76.0 tidyselect_1.2.1
## [85] chron_2.3-61 bit_4.0.5 compiler_4.3.2
## [88] curl_5.2.1 htmlTable_2.4.2 xml2_1.3.6
## [91] DelayedArray_0.28.0 shadowtext_0.1.3 rtracklayer_1.62.0
## [94] checkmate_2.3.1 scales_1.3.0 rappdirs_0.3.3
## [97] digest_0.6.35 rmarkdown_2.26 htmltools_0.5.8.1
## [100] pkgconfig_2.0.3 base64enc_0.1-3 highr_0.10
## [103] dbplyr_2.5.0 fastmap_1.2.0 rlang_1.1.3
## [106] GlobalOptions_0.1.2 htmlwidgets_1.6.4 farver_2.1.2
## [109] jquerylib_0.1.4 BiocParallel_1.36.0 R.oo_1.26.0
## [112] GOSemSim_2.28.1 RCurl_1.98-1.14 Formula_1.2-5
## [115] GenomeInfoDbData_1.2.11 munsell_0.5.1 Rcpp_1.0.12
## [118] proto_1.0.0 ape_5.8 babelgene_22.9
## [121] viridis_0.6.5 sqldf_0.4-11 stringi_1.8.4
## [124] ggraph_2.2.1 zlibbioc_1.48.2 MASS_7.3-60.0.1
## [127] org.Hs.eg.db_3.18.0 plyr_1.8.9 parallel_4.3.2
## [130] graphlayouts_1.1.1 splines_4.3.2 hms_1.1.3
## [133] igraph_2.0.3 ggsignif_0.6.4 futile.options_1.0.1
## [136] evaluate_0.23 lambda.r_1.2.4 BiocManager_1.30.23
## [139] tzdb_0.4.0 foreach_1.5.2 tweenr_2.0.3
## [142] polyclip_1.10-6 clue_0.3-65 ggforce_0.4.2
## [145] xtable_1.8-4 restfulr_0.0.15 tidytree_0.4.6
## [148] rstatix_0.7.2 ragg_1.3.2 viridisLite_0.4.2
## [151] aplot_0.2.2 memoise_2.0.1 GenomicAlignments_1.38.2
## [154] cluster_2.1.6 timechange_0.3.0